M
Michelangelo Paci
Researcher at Tampere University of Technology
Publications - 65
Citations - 1003
Michelangelo Paci is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Convolutional neural network & In silico. The author has an hindex of 13, co-authored 57 publications receiving 647 citations. Previous affiliations of Michelangelo Paci include University of Bologna & University of Tampere.
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Journal ArticleDOI
Computational models of ventricular- and atrial-like human induced pluripotent stem cell derived cardiomyocytes.
TL;DR: Two novel tools useful in investigating the electrophysiological features of hiPSC-CMs, whose importance is growing fast as in-vitro models for pharmacological studies are provided.
Journal ArticleDOI
General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy
Zhihua Li,Gary R. Mirams,Takashi Yoshinaga,Bradley J. Ridder,Xiaomei Han,Janell E. Chen,Norman Stockbridge,Todd Wisialowski,Bruce P. Damiano,Stefano Severi,Pierre Morissette,Peter R. Kowey,Mark Holbrook,Godfrey L. Smith,Randall L. Rasmusson,Michael Liu,Zhen Song,Zhilin Qu,Derek J. Leishman,Jill Steidl‐Nichols,Blanca Rodriguez,Alfonso Bueno-Orovio,Xin Zhou,Elisa Passini,Andrew G. Edwards,Stefano Morotti,Haibo Ni,Eleonora Grandi,Colleen E. Clancy,Jamie I. Vandenberg,Jamie I. Vandenberg,Adam P. Hill,Adam P. Hill,Mikiko Nakamura,Thomas Singer,Liudmila Polonchuk,Andrea Greiter-Wilke,Ken Wang,Stephane Nave,Aaron Fullerton,Eric A. Sobie,Michelangelo Paci,Flora Musuamba Tshinanu,David G. Strauss +43 more
TL;DR: This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration–sponsored Think Tank Meeting on May 22, 2018.
Journal ArticleDOI
Automatic Optimization of an in Silico Model of Human iPSC Derived Cardiomyocytes Recapitulating Calcium Handling Abnormalities.
Michelangelo Paci,Risto-Pekka Pölönen,Dario Cori,Kirsi Penttinen,Katriina Aalto-Setälä,Stefano Severi,Jari Hyttinen +6 more
TL;DR: A new and more versatile in silico model is presented, which will provide a platform for modeling the effects of drugs or mutations that affect Ca2+ handling in hiPSC-CMs.
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Data augmentation approaches for improving animal audio classification
TL;DR: This study shows that different CNNs can be trained for the purpose of animal audio classification and that their fusion works better than the stand-alone classifiers, and is the largest study on data augmentation for CNNs inAnimal audio classification audio datasets using the same set of classifiers and parameters.
Journal ArticleDOI
Human induced pluripotent stem cell‐derived versus adult cardiomyocytes: an in silico electrophysiological study on effects of ionic current block
TL;DR: This work systematically compared in silico the effects of selective ionic current block on hiPSC‐CM and hAdultV‐CM action potentials (APs), to identify similarities/differences and to illustrate the potential of computational models as supportive tools for evaluating new in vitro technologies.